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British Society for Population Studies 2007 Annual Conference St. Andrews, Scotland, 11-13 Sep 2007 Methodological Issue in Comparing the Size of Differences Between Rates of Experiencing or Avoiding an Outcome in Different Settings. James P. Scanlan Washington, DC jps@jpscanlan.com.
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British Society for Population Studies2007 Annual ConferenceSt. Andrews, Scotland, 11-13 Sep 2007Methodological Issue in Comparing the Size of Differences Between Rates of Experiencing or Avoiding an Outcome in Different Settings James P. Scanlan Washington, DC jps@jpscanlan.com
Four Binary Indicators of Difference 1 Relative differences between rates of experiencing an outcome (Ratio 1) 2 Relative differences between rates of avoiding an outcome (Ratio 2) 3 Odds ratios 4 Absolute differences between rates
Fig. 2: Ratio of (1) DG Fail Rate to AG Fail Rate and (2) AG Pass Rate to DG Pass Rate
Implications re (1) and (2) ● changes over time ● comparing inequalities in different populations or subpopulations - countries or regions - British civil servants vs. rest of UK - high-ed vs. low-ed US blacks and whites - young vs. old ● healthcare disparities ● comparing effects sizes ● what is a striking disparity?
Fig. 3: Ratio of (1) DG Fail Rate to AG Fail Rate, (2) AG Pass Rate to DG Pass Rate, (3) DG Fail Odds to AG Fail Odds ●
Fig. 4: Ratio of (1) DG Fail Rate to AG Fail Rate, (2) AG Pass Rate to DG Pass Rate, (3) DG Fail Odds to AG Fail Odds; plus (4) Absolute Difference Between Rates
Two Contrasting Studies • Trivedi et al. Trends in the quality of care and racial disparities in Medicare managed care. N Engl J Med 2005;353:692-700. • Jha et al. Racial trends in the use of major procedures among the elderly. N Engl J Med 2005;353:683-691.
Further analyses of quality and inequality • Trivedi et al. Relationship between quality of care and racial disparities in Medicare health plans. JAMA 2006;296:1998-2004. - correlations between quality and inequality - process outcomes versus clinical outcomes
Implications re (3) and (4) • As a favorable outcome increases in prevalence, absolute differences will tend to increase in Zone A and decline in Zone B and do both when there is crossover between zones • Opposite will occur when the outcome declines • Odds ratio will behave in the opposite manner • So what happens when you need the refinement of a logistic regression?
Fig. 5: Illustration with Near Normal Data – Based on Black and White Rates of Falling Below or Above Various Percentage of the Poverty Line
Fig. 6: Illustration with Normal Data (as in Fig 4) Limited to Universe Below Point Defined by 30 Percent Failure Rate of AG
Fig. 7: Illustration Based on Systolic Blood Pressure of Black and White Men Age 55 to 65 (from NHANES 1999-2000 and 2001-2002)
Fig. 8: Illustration Based on Systolic Blood Pressure (SBP) of Black and White Men Age 55 to 65 (from NHANES 1999-2000 and 2001-2002) – Limited to SBP > 139
Which measure is best? • None really indicates whether a change between rates is other than solely a consequence of changes in prevalence • Further, each can change in one direction even when there in fact is a meaningful change in the opposite direction
How can we measure inequalities over time? • Identifying departures from the standard, as, for example, when both relative differences change in the same direction (as discussed in the prior BSPS paper) - only useful when it happens (not often) - possible distributional irregularities make approach highly speculative
Other possibilities • Longevity – no (see BSPS paper) • SF 36 scores – no • Metabolic syndrome measures – no • Cardio risk indexes – no • Allostatic load – possibly • Components of allostatic load – possibly • Cortisol level – possibly • Self rated health on a continuous scale - possibly